A human-in-the-loop environment for developmental biology
Bibliographic record
Abstract
BOODLE (BiOlOgical DeveLopment Environment) is a long-term project to complement morphometric empirical studies in the field of developmental biology by means of interactive modelling and simulation techniques. BOODLE aims at providing viable behaviour models of cells that fit recorded time series of morphogenetic stages. This information is critical for driving empirical studies and for simulating the emergence of pathological abnormalities. In this paper, we present a BOODLE prototype that covers the whole functionality of the envisioned system. The application usage cycle starts with importing scan data of embryos of model organisms. Next, it allows the user/experimenter to work with the imported model, for instance to add meta data, to refine annotations, and even to automatically or manually populate the captured volumes with virtual cells. Finally, the user/experimenter is given the opportunity to run simulation experiments. To outline how the pipeline works in particular, we have setup a mockup toy experiment that optimises cell parameters such that a population of cells develops in accordance with several preset transient states. For all stages of the modelling and simulation process, BOODLE provides accessible interfaces and visualisations. These include visual programming and configuration of individual cells’ behaviours and physical properties. In this paper, we show how all these aspects together realise a prototype for a Biologist-in-the- Loop simulation for the creation, automated optimisation and analysis of cellular behaviour models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".